PLDA: Parallel Latent Dirichlet Allocation for Large-Scale Applications

@inproceedings{Wang2009PLDAPL,
  title={PLDA: Parallel Latent Dirichlet Allocation for Large-Scale Applications},
  author={Jue Wang and Hongjie Bai and Matt Stanton and Wen-Yen Chen and Edward Y. Chang},
  booktitle={AAIM},
  year={2009}
}
This paper presents PLDA, our parallel implementation of Latent Dirichlet Allocation on MPI and MapReduce. PLDA smooths out storage and computation bottlenecks and provides fault recovery for lengthy distributed computations. We show that PLDA can be applied to large, real-world applications and achieves good scalability. We have released MPI-PLDA to open source at http://code.google.com/p/plda under the Apache License. 
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